Software

WAECAST allows managers to input lake characteristics to determine the likelihood of stocking success based on data in the models in this study. Additionally, projections of Walleye stocking success that consider climate change are provided as part of the tool to understand the potential future impacts of lake warming.

This pipeline calculates a simple monthly water budget that includes water supply and consumptive use for thermoelectric, irrigation, and public supply for 12-digit Hydrologic Unit Codes (HUC12) across the conterminous United States for water years 2010-2020. Water budget results also include an assessment of supply and use imbalances within the context of historical climatic conditions...

Wildcat is a software tool to assess and map post-wildfire debris-flow hazards. The package can be run from the command line or within Python. This 1.0.0 release includes the core routines to: * Preprocess input datasets, * Design stream segment networks, * Estimate debris-flow hazards and rainfall thresholds, and * Export results to common GIS formats (such as Shapefiles and GeoJSON)...

The Google Earth Engine (GEE) code was written in JavaScript which can derive water area and water level time series data of multiple lakes for specified date range. Data derived from GEE code were used in classifying winter drawdown lakes and to derive winter drawdown metrics (using python code in Jupyter Notebook) in our submitted manuscript to the Journal of Environmental Management...

This code contains multiple scripts that (1) analyze Wood Thrush movement data, (2) simulate Wood Thrush movements in an individual-based model, and (3) analyze the results from these simulations.

A series of Jupyter notebooks describing the application of uniform manifold approximation and projection (UMAP) to streamflow in hydrologic catchments (examples given for Upper Colorado River Basin) and evaluation of manifolds from modeled and observational data. UMAP produces features that are robust, meaning the resulting images will look similar, even though the underlying data may...

For some sites and constituents the three dimensions of the Weighted Regressions on Time, Discharge, and Season (WRTDS) model do not capture all the important aspects of water quality variability. For example, in some locations hysteresis or antecedent flow conditions are important influences on water quality concentration and loads. In other places, daily flow is less predictive than...

The Chronic Wasting Disease Surveillance Optimization Software Version 2 computes sampling recommendations to detect chronic wasting disease (CWD) in white-tailed deer (Odocoileus virginianus). Driven by a combinatorial optimization algorithm, the software maximizes the return-on-investment to the wildlife agency while keeping the overall surveillance program within budget. The software...

This repository contains code to cluster a sample of Mussels based up their metabolic levels via Non-Metric Multidimensional Scaling (NMDS) and distance based-Redundancy Analysis (db-RDA). Mussel sample is broken down and examined by catch location, sex, and species. The code for this project assumes the reader is familiar with R and clustering techniques.

R-language code library to support netCDF I/O. This package was written to ease the process of reading and writing netcdfs in R for the Joint Ecosystem Modeling (JEM; https://jem.gov/) community. NetCDF files created using this package follow the Comprehensive Everglades Restoration Plan (CERP) data standards to facilitate data sharing among scientists in the Everglades area.

This repository contains code to simulate the use of YY-males and direct mortality such as harvest or pesticide applicaion as a control tools for different aquatic invasive species.

This model was written in python3 which can simulate water level fluctuations under different drawdown operation rules. We submitted the manuscript about this model to the Journal of Environmental Management. The model file was saved in directory “models/WDLake”, and you can reproduce the results by running the jupyter notebooks in “notebooks/”.